Search Results (3,234)

Industry 4.0 represents a significant transformation in industrial systems through digital integration, presenting both opportunities and challenges for aligning the workforce, especially in emerging economies like Thailand. This study adopts a sequential exploratory mixed-method approach to investigate how strategic human resource development (HRD) contributes to sustainable transformation, defined as the enduring alignment between workforce capabilities and technological advancement. The qualitative phase involved case studies of five Thai manufacturing firms at varying levels of Industry 4.0 adoption, utilizing semi-structured interviews with executives and HR leaders. Thematic findings informed the development of a structured survey, distributed to 144 firms. Partial Least Squares Structural Equation Modeling (PLS SEM) was used to test the hypothesized relationships among business pressures, leadership support, HRD preparedness, and technological readiness. The analysis reveals that business pressures significantly influence leadership and HRD, which in turn facilitate technological readiness. However, business pressures alone do not directly enhance readiness without the support of intermediaries. These results underscore the critical role of integrated HRD and leadership frameworks in enabling sustainable digital transformation. This study contributes to theoretical perspectives by integrating HRD, leadership, and technological readiness, offering practical guidance for firms aiming to navigate the complexities of Industry 4.0. Full article

This research addresses the challenges of proactive scheduling and routing in manufacturing systems governed by the Material Requirement Planning (MRP) method. Such systems often face capacity constraints, difficulties in resource balancing, and limited traceability of component requirements. The lack of seamless integration between customer orders and production tasks, combined with the manual and time-consuming nature of schedule adjustments, highlights the need for an automated and optimized scheduling method. We propose a novel optimization-based approach that leverages mixed-integer linear programming (MILP) combined with a proprietary procedure for reducing the size of the modeled problem to generate feasible and/or optimal production schedules. The model incorporates dynamic routing, partial resource utilization, limited additional resources (e.g., tools, workers), technological breaks, and time quantization. Key results include determining order feasibility, identifying unfulfilled order components, minimizing costs, shortening deadlines, and assessing feasibility in the absence of available resources. By automating the generation of data from MRP/ERP systems, constructing an optimization model, and exporting the results back to the MRP/ERP structure, this method improves decision-making and competes with expensive Advanced Planning and Scheduling (APS) systems. The proposed innovation solution—the integration of MILP-based optimization with the proprietary PT (data transformation) and PR (model-size reduction) procedures—not only increases operational efficiency but also enables demand source tracking and offers a scalable and economical alternative for modern production environments. Experimental results demonstrate significant reductions in production costs (up to 25%) and lead times (more than 50%). Full article

The Barcelona Metropolitan Area (AMB) has implemented various policies to reduce car use and promote more sustainable mobility. Initiatives such as superblocks, Low Emission Zones (LEZs), and the Bicivia network aim to transform the urban model in response to environmental and congestion challenges. However, the high reliance on private vehicles for intermunicipal travel, uneven infrastructure, and social resistance to certain changes remain significant issues. This study examines the evolution of mobility patterns and assesses the effectiveness of the above policies in fostering real and sustainable change. A mixed-methods approach was adopted, which combined an exploratory factor analysis (EFA) of 2011–2024 data, trend linear regression, and a comparative international analysis. The EFA identified four key structural dimensions: traditional transport infrastructure, active mobility and bus lines, public bicycles and mixed use, and transport efficiency and punctuality. The findings reveal a clear reduction in private car use and an increase in sustainable modes of transport. This indicates that there are prospects for future transformation. Nonetheless, challenges persist in intermunicipal mobility and the public acceptance of the measures. This study provides empirical and comparative evidence and emphasizes the need for integrated metropolitan governance to achieve a resilient and sustainable urban model. Full article

Effective postharvest cooling of olive fruit is increasingly critical under rising harvest temperatures driven by climate change. This study models passive cooling dynamics using a trait-based, mixed-effects statistical framework. Ten olive groups—representing seven cultivars and different ripening or size stages—were subjected to controlled cooling conditions. Surface temperature was recorded using infrared thermal imaging, and morphological and compositional traits were quantified. Temperature decay was modeled using Newton’s Law of Cooling, extended with a quadratic time term to capture nonlinear trajse thectories. A linear mixed-effects model was fitted to log-transformed, normalized temperature data, incorporating trait-by-time interactions and hierarchical random effects. The results confirmed that fruit weight, specific surface area (SSA), and specific heat capacity (SHC) are key drivers of cooling rate variability, consistent with theoretical expectations, but quantified here using a trait-based statistical model applied to olive fruit. The quadratic model consistently outperformed standard exponential models, revealing dynamic effects of traits on temperature decline. Residual variation at the group level pointed to additional unmeasured structural influences. This study demonstrates that olive fruit cooling behavior can be effectively predicted using interpretable, trait-dependent models. The findings offer a quantitative basis for optimizing postharvest cooling protocols and are particularly relevant for maintaining quality under high-temperature harvest conditions. Full article

Accurate estimation of Interrupted Sampling Repeater Jamming (ISRJ) sampling duration is essential for effective radar anti-jamming. However, in complex electromagnetic environments, the simultaneous presence of suppressive and deceptive jamming, coupled with significant signal overlap in the time–frequency domain, renders ISRJ separation and parameter estimation considerably challenging. To address this challenge, this paper proposes a method utilizing the Fractional Fourier Transform (FRFT) and a Hybrid Modality Fusion Network (HMFN) for ISRJ signal separation and sampling-duration estimation. The proposed method first employs FRFT and a time–frequency mask to separate the ISRJ and target echo from the mixed signal. This process effectively suppresses interference and extracts the ISRJ signal. Subsequently, an HMFN is employed for high-precision estimation of the ISRJ sampling duration, offering crucial parameter support for active electromagnetic countermeasures. Simulation results validate the performance of the proposed method. Specifically, even under strong interference conditions with a Signal-to-Jamming Ratio (SJR) of −5 dB for deceptive jamming and as low as −10 dB for suppressive jamming, the regression model’s coefficient of determination still reaches 0.91. This result clearly demonstrates the method’s robustness and effectiveness in complex electromagnetic environments. Full article

Hyperspectral imaging for seed vigor detection faces the challenges of handling high-dimensional spectral data, information loss after dimensionality reduction, and low feature differentiation between vigor levels. To address the above issues, this study proposes an improved dynamic optimize MDS (DO-MDS) dimensionality reduction algorithm based on multidimensional scaling transformation. DO-MDS better preserves key features between samples during dimensionality reduction. Secondly, a dual-stream spectral collaborative attention (DSCA) module is proposed. The DSCA module adopts a dual-modal fusion approach combining global feature capture and local feature enhancement, deepening the characterization capability of spectral features. This study selected commonly used rice seed varieties in Zhejiang Province and constructed three individual spectral datasets and a mixed dataset through aging, spectral acquisition, and germination experiments. The experiments involved using the DO-MDS processed datasets with a convolutional neural network embedded with the DSCA attention module, and the results demonstrate vigor discrimination accuracy rates of 93.85%, 93.4%, and 96.23% for the Chunyou 83, Zhongzao 39, and Zhongzu 53 datasets, respectively, achieving 94.8% for the mixed dataset. This study provides effective strategies for spectral dimensionality reduction in hyperspectral seed vigor detection and enhances the differentiation of spectral information for seeds with similar vigor levels. Full article

Sustainability in the construction sector has become a fundamental objective for mitigating escalating environmental challenges; given that concrete is the most widely used man-made material, extending its service life is therefore critical. Among durability concerns, magnesium sulfate (MgSO₄) attack is particularly deleterious to concrete structures. Therefore, this study investigates the short- and long-term performance of concrete produced with copper slag (CS)—a massive waste generated by copper mining activities worldwide—employed as a supplementary cementitious material (SCM), together with recycled coarse aggregate (RCA), obtained from concrete construction and demolition waste, when exposed to MgSO₄. CS was used as a 15 vol% cement replacement, while RCA was incorporated at 0%, 20%, 50%, and 100 vol%. Compressive strength, bulk density, water absorption, and porosity were measured after water curing (7–388 days) and following immersion in a 5 wt.% MgSO₄ solution for 180 and 360 days. Microstructural characteristics were assessed using scanning electron microscopy (SEM), X-ray diffraction (XRD), thermogravimetric analysis with its differential thermogravimetric derivative (TG-DTG), and Fourier transform infrared spectroscopy (FTIR) techniques. The results indicated that replacing 15% cement with CS reduced 7-day strength by ≤10%, yet parity with the reference mix was reached at 90 days. Strength losses increased monotonically with RCA content. Under MgSO₄ exposure, all mixtures experienced an initial compressive strength gain during the short-term exposures (28–100 days), attributed to the pore-filling effect of expansive sulfate phases. However, at long-term exposure (180–360 days), a clear strength decline was observed, mainly due to internal cracking, brucite formation, and the transformation of C–S–H into non-cementitious M–S–H gel. Based on these findings, the combined use of CS and RCA at low replacement levels shows potential for producing environmentally friendly concrete with mechanical and durability performance comparable to those of concrete made entirely with virgin materials. Full article

In the field of image dehazing, many deep learning-based methods have demonstrated promising results. However, these methods often neglect crucial frequency-domain information and rely heavily on labeled datasets, which limits their applicability to real-world hazy images. To address these issues, we propose a semi-supervised image-dehazing network based on a trusted library (WTS-Net). We construct a dual-branch wavelet transform network (DBWT-Net). It fuses high- and low-frequency features via a frequency-mixing module and enhances global context through attention mechanisms. Building on DBWT-Net, we embed this backbone in a teacher–student model to reduce reliance on labeled data. To enhance the reliability of the teacher network, we introduce a trusted library guided by NR-IQA. In addition, we employ a two-stage training strategy for the network. Experiments show that WTS-Net achieves superior generalization and robustness in both synthetic and real-world dehazing scenarios. Full article

The methanol oxidation reaction (MOR) of direct methanol fuel cells (DMFCs) is limited by the slow kinetic process and high reaction energy barrier, significantly restricting the commercial application of DMFCs. Therefore, developing MOR catalysts with high activity and stability is very important. In this paper, oxygen-functionalised activated carbon (FAC) with controllable oxygen-containing functional groups was prepared by adjusting the volume ratio of H₂SO₃/HNO₃ mixed acid, and Pd/AC and Pd/FAC catalysts were synthesised via the hydrazine hydrate reduction method. A series of characterisation techniques and electrochemical performance tests were used to study the catalyst. The results showed that when V(H₂SO₃):V(HNO₃) = 2:3, more defects were generated on the surface of the AC, and more oxygen-containing functional groups represented by C=O and C–OH were attached to the surface of the support, which increased the anchor sites of Pd and improved the dispersion of Pd nanoparticles (Pd NPs) on the support. At the same time, the mass–specific activity of Pd/FAC for MOR was 2320 mA·mg_Pd, which is 1.5 times that of Pd/AC, and the stability was also improved to a certain extent. In situ infrared spectroscopy further confirmed that oxygen functionalisation treatment promoted the formation and transformation of *COOH intermediates, accelerated the transformation of CO_L into CO_B, reduced the poisoning of CO_ads species adsorbed to the catalyst, optimised the reaction path and improved the catalytic kinetic performance. Full article

Historic cities across the globe have experienced profound changes in their spatial and functional characteristics over time, and the historic core of Patan, Nepal, is no exception. The area surrounding Patan Durbar Square was designated as a UNESCO World Heritage Site in 1979. Between 2003 and 2007, the Kathmandu Valley was placed on UNESCO’s List of World Heritage in Danger, largely due to various factors, including the rapid and unsympathetic transformation of its buffer zone. This study focuses on the Nagbahal neighborhood, a culturally significant locality within this buffer area, to explore a community-rooted and sustainable approach to conservation. Employing a mixed-methods research design, the study integrates qualitative and quantitative data gathered through interviews and surveys of native residents. It investigates the drivers and impacts of changes in the function, ownership, and physical form of traditional residential buildings, and assesses whether these changes align with principles of sustainable heritage conservation—social, cultural, economic, and environmental. While challenges persist, including the proliferation of reinforced concrete structures and limited enforcement of heritage policies, the findings reveal that Nagbahal remains resilient due to strong local traditions, active religious institutions, and cohesive social practices. The study offers transferable lessons for sustainable conservation in living heritage buffer zones globally. Full article

This article describes the interactions between radon emissions and tectonic movements that accompany seismic activity as a function of time. The interpretation is based on advanced data analysis methods, such as Fourier wavelet transform, SGolay correlation analysis, and time-based data categorization. The dataset comprised the measurement results of ²²²Rn activity concentrations and the effects of the tectonic activity of rock masses acquired from two water-tube tiltmeters and five SRDN-3 radon probes. The analysis included four seismic events with moderate and light magnitudes (≥4.0), with a hypocenter at a depth of 1–10 km, located approximately 75 km from the research site. Each seismic shock had a different distribution of rock mass phases recorded by the integrated (probe-tiltmeter) measurement system. The results indicate that at the research site, the radon-tectonic signal is best identified between 25 and 48 h and between 49 and 72 h before the seismic shock. Positive correlations between the tectonic signal and the radon signal associated with the tension phase in the rock mass and negative correlations between the tectonic signal and the radon signal associated with the compression phase allow the description of the behavior of the rock mass before the seismic shock. Mixed correlations (positive and negative) indicate that both the stress and strain phases of the rock mass are recorded. The observed correlations seem particularly promising, as they can be recorded already 1–3 days before the seismic event, allowing an appropriately early response to the expected seismic event. Full article

The integration of extended reality (XR) technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), is transforming forensic investigation by empowering processes such as crime scene reconstruction, evidence analysis, and professional training. This manuscript presents a systematic review of technological advances in XR technologies developed and employed for forensic investigation, their impacts, challenges, and prospects for the future. A systematic review was carried out based on the PRISMA^® methodology and considering articles published in repositories and scientific databases such as SCOPUS, Science Direct, PubMed, Web of Science, Taylor and Francis, and IEEE Xplore. Two observers carried out the selection of articles and a Cohen’s Kappa coefficient of 0.7226 (substantial agreement) was evaluated. The results show that XR technologies contribute to improving accuracy, efficiency, and collaboration in forensic investigation processes. In addition, they facilitate the preservation of crime scene data and reduce training costs. Technological limitations, implementation costs, ethical aspects, and challenges persist in the acceptability of these devices. XR technologies have significant transformative potential in forensic investigations, although additional research is required to overcome current barriers and establish standardized protocols that enable their effective integration. Full article

Accurate and real-time classification of low-latency Internet traffic is critical for applications such as video conferencing, online gaming, financial trading, and autonomous systems, where millisecond-level delays can degrade user experience. Existing methods for low-latency traffic classification, reliant on raw temporal features or static statistical analyses, fail to capture dynamic frequency patterns inherent to real-time applications. These limitations hinder accurate resource allocation in heterogeneous networks. This paper proposes a novel framework integrating wavelet transform (WT) and artificial neural networks (ANNs) to address this gap. Unlike prior works, we systematically apply WT to commonly used temporal features—such as throughput, slope, ratio, and moving averages—transforming them into frequency-domain representations. This approach reveals hidden multi-scale patterns in low-latency traffic, akin to structured noise in signal processing, which traditional time-domain analyses often overlook. These wavelet-enhanced features train a multilayer perceptron (MLP) ANN, enabling dual-domain (time–frequency) analysis. We evaluate our approach on a dataset comprising FTP, video streaming, and low-latency traffic, including mixed scenarios with up to four concurrent traffic types. Experiments demonstrate 99.56% accuracy in distinguishing low-latency traffic (e.g., video conferencing) from FTP and streaming, outperforming k-NN, CNNs, and LSTMs. Notably, our method eliminates reliance on deep packet inspection (DPI), offering ISPs a privacy-preserving and scalable solution for prioritizing time-sensitive traffic. In mixed-traffic scenarios, the model achieves 74.2–92.8% accuracy, offering ISPs a scalable solution for prioritizing time-sensitive traffic without deep packet inspection. By bridging signal processing and deep learning, this work advances efficient bandwidth allocation and enables Internet Service Providers to prioritize time-sensitive flows without deep packet inspection, improving quality of service in heterogeneous network environments. Full article

The integration of Lesson Study (LS) and Didactic Suitability Criteria (DSC) presents an innovative framework for enhancing mathematics teacher training in higher education. This study examines how LS-DSC fosters instructional refinement, professional growth, and pedagogical transformation among in-service teachers. Using a quasi-experimental mixed-methods approach, the study analyzed data from 520 mathematics educators participating in a six-month training program incorporating collaborative lesson planning, structured pedagogical assessment, and reflective teaching practices. Findings indicate significant improvements in instructional design, mathematical discourse facilitation, and adaptive teaching strategies, with post-training evaluations demonstrating a strong positive correlation (r = 0.78) between initial competency levels and learning gains. Participants reported increased confidence in implementing student-centered methodologies and sustained engagement in peer collaboration beyond the training period. The results align with prior research emphasizing the effectiveness of lesson study models and structured evaluation frameworks in teacher professionalization. This study contributes to higher education policy and practice by advocating for the institutional adoption of LS-DSC methodologies to promote evidence-based professional development. Future research should explore the long-term scalability of LS-DSC in diverse educational contexts and its impact on student learning outcomes. Full article

Water environments and sediments are important reservoirs for antibiotic resistance genes (ARGs). Under the pressure of antibiotics, ARGs can transform between microorganisms. Lanthanum-modified bentonite (LMB) is a phosphorus passivation material with good prospects in water environment restoration. After a treatment with LMB, the phosphorus forms in water and sediments will change, which may have an impact on microorganisms and the transmission of ARGs. To investigate the effects of LMB and antibiotics on ARGs and bacterial communities in sediment and aquatic environments, LMB and tetracycline (Tet) were added individually and in combination to mixed samples of sediment and water. The results showed that the addition of either LMB or Tet increased the abundance of intI1 and tetA genes in both the sediment and water, with the Tet addition increasing ARGs to more than 1.5 times the abundance in the control group. However, when LMB and Tet were present simultaneously, the abundance of ARGs showed no significant difference compared to the control group. Tet and LMB also affected the bacterial community structure and function in the samples and had different effects on the sediment and water. A correlation analysis revealed that the potential host bacteria of the intI1 and tetA genes were unclassified_Geobacteraceae, Geothrix, Flavobacterium, Anaeromyxobacter, and Geothermobacter. These findings indicate that Tet or LMB may increase the dissemination of ARGs by affecting microbial communities, while LMB may reduce the impact of Tet through adsorption, providing a reference for the safety of the LMB application in the environment and its other effects (alleviating antibiotic pollution) in addition to phosphorus removal. Full article